In classical mechanical engineering the predominant group of system analysis and identification tools relies on Linear Systems, where research have been carried out for over half a century. Usage of Linear Systems is most widely spread, often due to its simple mathematics and formulation for many engineering problems. Although linearizing is a means for simplifying a problem, it will introduce more or less severe modelling errors. In some cases the errors due to linearizing are too large to be practically acceptable, and therefore nonlinear structures and models are sometimes introduced.

This thesis aims in implementing and evaluating some popular methods and algorithms for nonlinear structure analysis and identification, with emphasis on systems having nonlinear terms. Preferably the algorithms should be optimized in their computational load.

The result are several algorithms for nonlinear analysis and identification. The ones giving best results were the frequency based methods Reverse Path and a Frequency Domain Structure Selection Method (FDSSA). The time domain based method, Nonlinear Autoregressive Moving Average with Exogenous Input (NARMAX), in which a lot of hope had been put, did perform very well in giving good system descriptions, but due to its nonphysical representation it was not suitable for usage in this thesis.

The algorithms and methods were finally applied for two cases, a four system black-box case and an experimental test-rig case. The methods did perform well in three out of four systems in the first case, but the methods did not perform well for the second case, due to problems in applying correct levels of excitation force at the test-rig’s resonance frequencies.